The present disclosure relates to an apparatus and method for enhancing image quality of an input image captured by an image capturing device such as a camcorder or a camera, and more particularly, to an apparatus and method for enhancing image quality of an input image, which has been captured by using a multiple color-filter aperture (MCA) camera based on a digital multi-focusing color shift model, by using a cluster-based image segmentation algorithm.
A digital auto-focusing technique has increasingly come to prominence in various applications, such as super-resolution, video surveillance, and image signal processing chain in a digital camera, and the like. However, most auto-focusing techniques require intensive computational overhead such as point spread function (PSF) estimation, digital image restoration, post-processing for suppressing amplified noise, or the like.
Recently, an increase in the resolution and precision of image devices brings about making a focal point of an image blurry due to a fine shift of an object or an image capturing device in capturing an image of the object. In particular, when a plurality of objects located at different distances from a lens exist, the focuses of the objects, except the object located at the focal distance, are not precisely adjusted, causing blurs. Thus, the necessity of an auto-focusing technique for restoring the images out of focus is increasing.
The related arts for an auto-focusing generally include two modules: an analysis module and a control module. The analysis module estimates a focus degree of an image on the plane of the image, and the control module serves to move a lens assembly to an optical focus position based on the focus degree information estimated by the analysis module to adjust the focus. The systems employing the auto-focusing technique include an infrared auto-focusing (IRAF) system, a TTL (through-the-lens auto-focusing (TTLAF) system, a semi-digital auto-focusing (SDAF) system, and a fully digital auto-focusing (FDAF) system. The IRAF scheme uses infrared rays, the TTLAF scheme uses a phase difference of an input image, and the SDAF scheme uses a high frequency of an input image to estimate the depth of field. The FDAF scheme generally includes a digital image restoration and fusion process to obtain an image in focus. In terms of the precision, the FIRAF scheme has good precision, and the TTLAF scheme can obtain very high quality factor when good conditions are met. Meanwhile, the SDAF and the FDAF schemes have a less precision than that of the FIRAF and TTLAF schemes.
The FDAF scheme has a limitation related to a digital image restoration. When objects of the depths of fields are restored, a reblurring or ringing phenomenon may occur at the focus regions, and the depths of fields may be lowered. For the same depths of fields, there is a remarkable limitation in the image quality of the restored image due to the image restoration. Also, much time is required to obtain a fully restored image due to the repeated restoration process.
Thus, a technique for performing autofocusing function at a high speed by reducing the amount of calculation for autofocusing and reliably adjusting the focus of all the objects existing at different locations even in a multi-focus image is required.